Overview

Dataset statistics

Number of variables45
Number of observations314
Missing cells0
Missing cells (%)0.0%
Total size in memory38.7 KiB
Average record size in memory126.2 B

Variable types

Categorical24
Numeric21

Alerts

calidad_produc is highly overall correlated with calif_vozHigh correlation
calif_voz is highly overall correlated with calidad_produc and 2 other fieldsHigh correlation
senal_voz is highly overall correlated with calif_voz and 1 other fieldsHigh correlation
estabil_llamada is highly overall correlated with calif_voz and 1 other fieldsHigh correlation
recharges_month_a is highly overall correlated with calls_out_month_p and 3 other fieldsHigh correlation
nr_recharges_month_a is highly overall correlated with recharges_month_a and 2 other fieldsHigh correlation
paq_990_m1 is highly overall correlated with max_rech and 1 other fieldsHigh correlation
consumo_granel_m1 is highly overall correlated with max_network_voice and 2 other fieldsHigh correlation
vlr_cargas_m1 is highly overall correlated with calls_drop_s and 4 other fieldsHigh correlation
cant_cargas_m1 is highly overall correlated with calls_drop_s and 4 other fieldsHigh correlation
calls_drop_s is highly overall correlated with calls_failure_s and 2 other fieldsHigh correlation
calls_failure_s is highly overall correlated with calls_drop_s and 2 other fieldsHigh correlation
calls_out_month_p is highly overall correlated with recharges_month_aHigh correlation
technology is highly overall correlated with max_network_voice and 1 other fieldsHigh correlation
max_network_voice is highly overall correlated with technology and 2 other fieldsHigh correlation
paq_datos_m1 is highly overall correlated with max_rechHigh correlation
max_rech is highly overall correlated with max_network_voice and 3 other fieldsHigh correlation
band_7 is highly overall correlated with technologyHigh correlation
antigued is highly imbalanced (79.8%)Imbalance
paq_bund_m1 is highly imbalanced (69.6%)Imbalance
consumos_voz_m1 is highly imbalanced (51.5%)Imbalance
pyp_m1 is highly imbalanced (69.6%)Imbalance
promo_recarga_m1 is highly imbalanced (52.5%)Imbalance
dia_sorpresa_m1 is highly imbalanced (88.2%)Imbalance
calls_in_tot_p has unique valuesUnique
duration_all_in_a has unique valuesUnique
calls_drop_s has 112 (35.7%) zerosZeros
calls_failure_s has 86 (27.4%) zerosZeros
no_answer_calls_p has 5 (1.6%) zerosZeros
setup_failure_perc_a has 56 (17.8%) zerosZeros
dropped_calls_perc_a has 64 (20.4%) zerosZeros
recharges_month_a has 22 (7.0%) zerosZeros
nr_recharges_month_a has 22 (7.0%) zerosZeros
vlr_cargas_m1 has 36 (11.5%) zerosZeros
cant_cargas_m1 has 36 (11.5%) zerosZeros

Reproduction

Analysis started2023-07-11 01:05:36.392120
Analysis finished2023-07-11 01:07:51.084102
Duration2 minutes and 14.69 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

antigued
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Más de 2 años
296 
Entre 1 y 2 años
 
8
Entre 2 meses y 6 meses
 
6
Entre 7 meses y 1 año
 
4

Length

Max length23
Median length13
Mean length13.369427
Min length13

Characters and Unicode

Total characters4198
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMás de 2 años
2nd rowMás de 2 años
3rd rowMás de 2 años
4th rowMás de 2 años
5th rowMás de 2 años

Common Values

ValueCountFrequency (%)
Más de 2 años 296
94.3%
Entre 1 y 2 años 8
 
2.5%
Entre 2 meses y 6 meses 6
 
1.9%
Entre 7 meses y 1 año 4
 
1.3%

Length

2023-07-11T01:07:51.256675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:51.710107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 310
24.1%
años 304
23.7%
más 296
23.1%
de 296
23.1%
entre 18
 
1.4%
y 18
 
1.4%
meses 16
 
1.2%
1 12
 
0.9%
6 6
 
0.5%
7 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
970
23.1%
s 632
15.1%
e 346
 
8.2%
2 310
 
7.4%
o 308
 
7.3%
ñ 308
 
7.3%
a 308
 
7.3%
á 296
 
7.1%
M 296
 
7.1%
d 296
 
7.1%
Other values (9) 128
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2582
61.5%
Space Separator 970
 
23.1%
Decimal Number 332
 
7.9%
Uppercase Letter 314
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 632
24.5%
e 346
13.4%
o 308
11.9%
ñ 308
11.9%
a 308
11.9%
á 296
11.5%
d 296
11.5%
n 18
 
0.7%
t 18
 
0.7%
r 18
 
0.7%
Other values (2) 34
 
1.3%
Decimal Number
ValueCountFrequency (%)
2 310
93.4%
1 12
 
3.6%
6 6
 
1.8%
7 4
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
M 296
94.3%
E 18
 
5.7%
Space Separator
ValueCountFrequency (%)
970
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2896
69.0%
Common 1302
31.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 632
21.8%
e 346
11.9%
o 308
10.6%
ñ 308
10.6%
a 308
10.6%
á 296
10.2%
M 296
10.2%
d 296
10.2%
E 18
 
0.6%
n 18
 
0.6%
Other values (4) 70
 
2.4%
Common
ValueCountFrequency (%)
970
74.5%
2 310
 
23.8%
1 12
 
0.9%
6 6
 
0.5%
7 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3594
85.6%
None 604
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
970
27.0%
s 632
17.6%
e 346
 
9.6%
2 310
 
8.6%
o 308
 
8.6%
a 308
 
8.6%
M 296
 
8.2%
d 296
 
8.2%
E 18
 
0.5%
n 18
 
0.5%
Other values (7) 92
 
2.6%
None
ValueCountFrequency (%)
ñ 308
51.0%
á 296
49.0%

grupo_edad
Categorical

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
De 26 a 35 años
83 
De 46 a 55 años
67 
De 36 a 45 años
66 
De 18 a 25 años
57 
De 56 a 65 años
36 

Length

Max length15
Median length15
Mean length14.904459
Min length9

Characters and Unicode

Total characters4680
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDe 36 a 45 años
2nd rowDe 56 a 65 años
3rd rowDe 46 a 55 años
4th rowDe 26 a 35 años
5th rowDe 36 a 45 años

Common Values

ValueCountFrequency (%)
De 26 a 35 años 83
26.4%
De 46 a 55 años 67
21.3%
De 36 a 45 años 66
21.0%
De 18 a 25 años 57
18.2%
De 56 a 65 años 36
11.5%
Más de 66 5
 
1.6%

Length

2023-07-11T01:07:52.179225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:52.692463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
de 314
20.1%
a 309
19.8%
años 309
19.8%
26 83
 
5.3%
35 83
 
5.3%
46 67
 
4.3%
55 67
 
4.3%
36 66
 
4.2%
45 66
 
4.2%
18 57
 
3.7%
Other values (5) 139
8.9%

Most occurring characters

ValueCountFrequency (%)
1246
26.6%
a 618
13.2%
5 412
 
8.8%
s 314
 
6.7%
e 314
 
6.7%
D 309
 
6.6%
o 309
 
6.6%
ñ 309
 
6.6%
6 298
 
6.4%
3 149
 
3.2%
Other values (7) 402
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1874
40.0%
Space Separator 1246
26.6%
Decimal Number 1246
26.6%
Uppercase Letter 314
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 618
33.0%
s 314
16.8%
e 314
16.8%
o 309
16.5%
ñ 309
16.5%
á 5
 
0.3%
d 5
 
0.3%
Decimal Number
ValueCountFrequency (%)
5 412
33.1%
6 298
23.9%
3 149
 
12.0%
2 140
 
11.2%
4 133
 
10.7%
1 57
 
4.6%
8 57
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
D 309
98.4%
M 5
 
1.6%
Space Separator
ValueCountFrequency (%)
1246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2492
53.2%
Latin 2188
46.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 618
28.2%
s 314
14.4%
e 314
14.4%
D 309
14.1%
o 309
14.1%
ñ 309
14.1%
M 5
 
0.2%
á 5
 
0.2%
d 5
 
0.2%
Common
ValueCountFrequency (%)
1246
50.0%
5 412
 
16.5%
6 298
 
12.0%
3 149
 
6.0%
2 140
 
5.6%
4 133
 
5.3%
1 57
 
2.3%
8 57
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4366
93.3%
None 314
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1246
28.5%
a 618
14.2%
5 412
 
9.4%
s 314
 
7.2%
e 314
 
7.2%
D 309
 
7.1%
o 309
 
7.1%
6 298
 
6.8%
3 149
 
3.4%
2 140
 
3.2%
Other values (5) 257
 
5.9%
None
ValueCountFrequency (%)
ñ 309
98.4%
á 5
 
1.6%

gener
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
FemeniNO
172 
MasculiNO
142 

Length

Max length9
Median length8
Mean length8.4522293
Min length8

Characters and Unicode

Total characters2654
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculiNO
2nd rowMasculiNO
3rd rowFemeniNO
4th rowFemeniNO
5th rowFemeniNO

Common Values

ValueCountFrequency (%)
FemeniNO 172
54.8%
MasculiNO 142
45.2%

Length

2023-07-11T01:07:53.214252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:53.647324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
femenino 172
54.8%
masculino 142
45.2%

Most occurring characters

ValueCountFrequency (%)
e 344
13.0%
i 314
11.8%
N 314
11.8%
O 314
11.8%
F 172
6.5%
m 172
6.5%
n 172
6.5%
M 142
 
5.4%
a 142
 
5.4%
s 142
 
5.4%
Other values (3) 426
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1712
64.5%
Uppercase Letter 942
35.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 344
20.1%
i 314
18.3%
m 172
10.0%
n 172
10.0%
a 142
8.3%
s 142
8.3%
c 142
8.3%
u 142
8.3%
l 142
8.3%
Uppercase Letter
ValueCountFrequency (%)
N 314
33.3%
O 314
33.3%
F 172
18.3%
M 142
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2654
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 344
13.0%
i 314
11.8%
N 314
11.8%
O 314
11.8%
F 172
6.5%
m 172
6.5%
n 172
6.5%
M 142
 
5.4%
a 142
 
5.4%
s 142
 
5.4%
Other values (3) 426
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 344
13.0%
i 314
11.8%
N 314
11.8%
O 314
11.8%
F 172
6.5%
m 172
6.5%
n 172
6.5%
M 142
 
5.4%
a 142
 
5.4%
s 142
 
5.4%
Other values (3) 426
16.1%

estado_civil
Categorical

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
SOLTERO (A)
103 
UNIÓN LIBRE
101 
CASADO (A)
90 
DIVORCIADO (A)
12 
NO RESPONDE
 
4

Length

Max length14
Median length11
Mean length10.802548
Min length9

Characters and Unicode

Total characters3392
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNIÓN LIBRE
2nd rowSOLTERO (A)
3rd rowUNIÓN LIBRE
4th rowDIVORCIADO (A)
5th rowUNIÓN LIBRE

Common Values

ValueCountFrequency (%)
SOLTERO (A) 103
32.8%
UNIÓN LIBRE 101
32.2%
CASADO (A) 90
28.7%
DIVORCIADO (A) 12
 
3.8%
NO RESPONDE 4
 
1.3%
VIUDO (A) 4
 
1.3%

Length

2023-07-11T01:07:54.000375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:54.452867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 209
33.3%
soltero 103
16.4%
unión 101
16.1%
libre 101
16.1%
casado 90
14.3%
divorciado 12
 
1.9%
no 4
 
0.6%
responde 4
 
0.6%
viudo 4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A 401
11.8%
O 332
 
9.8%
314
 
9.3%
I 230
 
6.8%
R 220
 
6.5%
E 212
 
6.2%
N 210
 
6.2%
) 209
 
6.2%
( 209
 
6.2%
L 204
 
6.0%
Other values (9) 851
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2660
78.4%
Space Separator 314
 
9.3%
Close Punctuation 209
 
6.2%
Open Punctuation 209
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 401
15.1%
O 332
12.5%
I 230
8.6%
R 220
8.3%
E 212
8.0%
N 210
7.9%
L 204
7.7%
S 197
7.4%
D 122
 
4.6%
U 105
 
3.9%
Other values (6) 427
16.1%
Space Separator
ValueCountFrequency (%)
314
100.0%
Close Punctuation
ValueCountFrequency (%)
) 209
100.0%
Open Punctuation
ValueCountFrequency (%)
( 209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2660
78.4%
Common 732
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 401
15.1%
O 332
12.5%
I 230
8.6%
R 220
8.3%
E 212
8.0%
N 210
7.9%
L 204
7.7%
S 197
7.4%
D 122
 
4.6%
U 105
 
3.9%
Other values (6) 427
16.1%
Common
ValueCountFrequency (%)
314
42.9%
) 209
28.6%
( 209
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3291
97.0%
None 101
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 401
12.2%
O 332
10.1%
314
9.5%
I 230
 
7.0%
R 220
 
6.7%
E 212
 
6.4%
N 210
 
6.4%
) 209
 
6.4%
( 209
 
6.4%
L 204
 
6.2%
Other values (8) 750
22.8%
None
ValueCountFrequency (%)
Ó 101
100.0%

region
Categorical

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Centro
153 
Noroccidente
55 
Costa
49 
Suroccidente
42 
Oriente
 
11

Length

Max length12
Median length10
Mean length7.7834395
Min length5

Characters and Unicode

Total characters2444
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCosta
2nd rowSuroccidente
3rd rowCentro
4th rowCentro
5th rowCentro

Common Values

ValueCountFrequency (%)
Centro 153
48.7%
Noroccidente 55
 
17.5%
Costa 49
 
15.6%
Suroccidente 42
 
13.4%
Oriente 11
 
3.5%
Suroriente 4
 
1.3%

Length

2023-07-11T01:07:54.942767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:55.261757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
centro 153
48.7%
noroccidente 55
 
17.5%
costa 49
 
15.6%
suroccidente 42
 
13.4%
oriente 11
 
3.5%
suroriente 4
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 377
15.4%
o 358
14.6%
t 314
12.8%
r 269
11.0%
n 265
10.8%
C 202
8.3%
c 194
7.9%
i 112
 
4.6%
d 97
 
4.0%
N 55
 
2.3%
Other values (5) 201
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2130
87.2%
Uppercase Letter 314
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 377
17.7%
o 358
16.8%
t 314
14.7%
r 269
12.6%
n 265
12.4%
c 194
9.1%
i 112
 
5.3%
d 97
 
4.6%
s 49
 
2.3%
a 49
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 202
64.3%
N 55
 
17.5%
S 46
 
14.6%
O 11
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2444
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 377
15.4%
o 358
14.6%
t 314
12.8%
r 269
11.0%
n 265
10.8%
C 202
8.3%
c 194
7.9%
i 112
 
4.6%
d 97
 
4.0%
N 55
 
2.3%
Other values (5) 201
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 377
15.4%
o 358
14.6%
t 314
12.8%
r 269
11.0%
n 265
10.8%
C 202
8.3%
c 194
7.9%
i 112
 
4.6%
d 97
 
4.0%
N 55
 
2.3%
Other values (5) 201
8.2%

calidad_produc
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0605096
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:07:55.507842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.404635
Coefficient of variation (CV)0.34057527
Kurtosis0.10006836
Mean7.0605096
Median Absolute Deviation (MAD)1
Skewness-0.85276258
Sum2217
Variance5.7822694
MonotonicityNot monotonic
2023-07-11T01:07:55.721597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 70
22.3%
9 50
15.9%
10 48
15.3%
7 38
12.1%
5 36
11.5%
6 28
 
8.9%
4 16
 
5.1%
1 15
 
4.8%
3 8
 
2.5%
2 5
 
1.6%
ValueCountFrequency (%)
1 15
 
4.8%
2 5
 
1.6%
3 8
 
2.5%
4 16
 
5.1%
5 36
11.5%
6 28
 
8.9%
7 38
12.1%
8 70
22.3%
9 50
15.9%
10 48
15.3%
ValueCountFrequency (%)
10 48
15.3%
9 50
15.9%
8 70
22.3%
7 38
12.1%
6 28
 
8.9%
5 36
11.5%
4 16
 
5.1%
3 8
 
2.5%
2 5
 
1.6%
1 15
 
4.8%

calif_voz
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6496815
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:07:55.961138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median8
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2928316
Coefficient of variation (CV)0.29972902
Kurtosis0.46486957
Mean7.6496815
Median Absolute Deviation (MAD)2
Skewness-1.0353309
Sum2402
Variance5.2570766
MonotonicityNot monotonic
2023-07-11T01:07:56.180370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 84
26.8%
8 62
19.7%
9 50
15.9%
7 40
12.7%
5 24
 
7.6%
6 21
 
6.7%
4 11
 
3.5%
2 8
 
2.5%
3 8
 
2.5%
1 6
 
1.9%
ValueCountFrequency (%)
1 6
 
1.9%
2 8
 
2.5%
3 8
 
2.5%
4 11
 
3.5%
5 24
 
7.6%
6 21
 
6.7%
7 40
12.7%
8 62
19.7%
9 50
15.9%
10 84
26.8%
ValueCountFrequency (%)
10 84
26.8%
9 50
15.9%
8 62
19.7%
7 40
12.7%
6 21
 
6.7%
5 24
 
7.6%
4 11
 
3.5%
3 8
 
2.5%
2 8
 
2.5%
1 6
 
1.9%

senal_voz
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2452229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:07:56.406327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4338651
Coefficient of variation (CV)0.33592687
Kurtosis-0.15872159
Mean7.2452229
Median Absolute Deviation (MAD)2
Skewness-0.79922019
Sum2275
Variance5.9236992
MonotonicityNot monotonic
2023-07-11T01:07:56.618083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 67
21.3%
8 56
17.8%
9 49
15.6%
7 41
13.1%
5 31
9.9%
6 25
 
8.0%
4 17
 
5.4%
2 12
 
3.8%
3 8
 
2.5%
1 8
 
2.5%
ValueCountFrequency (%)
1 8
 
2.5%
2 12
 
3.8%
3 8
 
2.5%
4 17
 
5.4%
5 31
9.9%
6 25
 
8.0%
7 41
13.1%
8 56
17.8%
9 49
15.6%
10 67
21.3%
ValueCountFrequency (%)
10 67
21.3%
9 49
15.6%
8 56
17.8%
7 41
13.1%
6 25
 
8.0%
5 31
9.9%
4 17
 
5.4%
3 8
 
2.5%
2 12
 
3.8%
1 8
 
2.5%

estabil_llamada
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.366242
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:07:56.841149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4552754
Coefficient of variation (CV)0.33331452
Kurtosis-0.32329647
Mean7.366242
Median Absolute Deviation (MAD)2
Skewness-0.81193628
Sum2313
Variance6.0283775
MonotonicityNot monotonic
2023-07-11T01:07:57.067474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 72
22.9%
9 63
20.1%
8 49
15.6%
5 33
10.5%
6 27
 
8.6%
7 27
 
8.6%
2 16
 
5.1%
4 13
 
4.1%
3 10
 
3.2%
1 4
 
1.3%
ValueCountFrequency (%)
1 4
 
1.3%
2 16
 
5.1%
3 10
 
3.2%
4 13
 
4.1%
5 33
10.5%
6 27
 
8.6%
7 27
 
8.6%
8 49
15.6%
9 63
20.1%
10 72
22.9%
ValueCountFrequency (%)
10 72
22.9%
9 63
20.1%
8 49
15.6%
7 27
 
8.6%
6 27
 
8.6%
5 33
10.5%
4 13
 
4.1%
3 10
 
3.2%
2 16
 
5.1%
1 4
 
1.3%

uso_serv_cliente
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
NO
189 
SI
125 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters628
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 189
60.2%
SI 125
39.8%

Length

2023-07-11T01:07:57.330910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:57.580073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 189
60.2%
si 125
39.8%

Most occurring characters

ValueCountFrequency (%)
N 189
30.1%
O 189
30.1%
S 125
19.9%
I 125
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 628
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 189
30.1%
O 189
30.1%
S 125
19.9%
I 125
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 628
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 189
30.1%
O 189
30.1%
S 125
19.9%
I 125
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 189
30.1%
O 189
30.1%
S 125
19.9%
I 125
19.9%

data_usur
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
NO
187 
SI
127 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters628
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowNO
3rd rowNO
4th rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
NO 187
59.6%
SI 127
40.4%

Length

2023-07-11T01:07:57.792189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:58.069952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 187
59.6%
si 127
40.4%

Most occurring characters

ValueCountFrequency (%)
N 187
29.8%
O 187
29.8%
S 127
20.2%
I 127
20.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 628
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 187
29.8%
O 187
29.8%
S 127
20.2%
I 127
20.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 628
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 187
29.8%
O 187
29.8%
S 127
20.2%
I 127
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 187
29.8%
O 187
29.8%
S 127
20.2%
I 127
20.2%

technology
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
3G
159 
2G
89 
LTE
66 

Length

Max length3
Median length2
Mean length2.2101911
Min length2

Characters and Unicode

Total characters694
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2G
2nd row2G
3rd row3G
4th row3G
5th rowLTE

Common Values

ValueCountFrequency (%)
3G 159
50.6%
2G 89
28.3%
LTE 66
21.0%

Length

2023-07-11T01:07:58.285277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:58.563441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3g 159
50.6%
2g 89
28.3%
lte 66
21.0%

Most occurring characters

ValueCountFrequency (%)
G 248
35.7%
3 159
22.9%
2 89
 
12.8%
L 66
 
9.5%
T 66
 
9.5%
E 66
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 446
64.3%
Decimal Number 248
35.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 248
55.6%
L 66
 
14.8%
T 66
 
14.8%
E 66
 
14.8%
Decimal Number
ValueCountFrequency (%)
3 159
64.1%
2 89
35.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 446
64.3%
Common 248
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 248
55.6%
L 66
 
14.8%
T 66
 
14.8%
E 66
 
14.8%
Common
ValueCountFrequency (%)
3 159
64.1%
2 89
35.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 248
35.7%
3 159
22.9%
2 89
 
12.8%
L 66
 
9.5%
T 66
 
9.5%
E 66
 
9.5%

band_7
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
NO
250 
SI
64 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters628
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowSI

Common Values

ValueCountFrequency (%)
NO 250
79.6%
SI 64
 
20.4%

Length

2023-07-11T01:07:58.781752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:59.066599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 250
79.6%
si 64
 
20.4%

Most occurring characters

ValueCountFrequency (%)
N 250
39.8%
O 250
39.8%
S 64
 
10.2%
I 64
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 628
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 250
39.8%
O 250
39.8%
S 64
 
10.2%
I 64
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 628
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 250
39.8%
O 250
39.8%
S 64
 
10.2%
I 64
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 250
39.8%
O 250
39.8%
S 64
 
10.2%
I 64
 
10.2%

max_network_voice
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Network 3G
212 
Network 2G
102 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3140
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNetwork 3G
2nd rowNetwork 2G
3rd rowNetwork 3G
4th rowNetwork 3G
5th rowNetwork 3G

Common Values

ValueCountFrequency (%)
Network 3G 212
67.5%
Network 2G 102
32.5%

Length

2023-07-11T01:07:59.283663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:07:59.542607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
network 314
50.0%
3g 212
33.8%
2g 102
 
16.2%

Most occurring characters

ValueCountFrequency (%)
N 314
10.0%
e 314
10.0%
t 314
10.0%
w 314
10.0%
o 314
10.0%
r 314
10.0%
k 314
10.0%
314
10.0%
G 314
10.0%
3 212
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1884
60.0%
Uppercase Letter 628
 
20.0%
Space Separator 314
 
10.0%
Decimal Number 314
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 314
16.7%
t 314
16.7%
w 314
16.7%
o 314
16.7%
r 314
16.7%
k 314
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 314
50.0%
G 314
50.0%
Decimal Number
ValueCountFrequency (%)
3 212
67.5%
2 102
32.5%
Space Separator
ValueCountFrequency (%)
314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2512
80.0%
Common 628
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 314
12.5%
e 314
12.5%
t 314
12.5%
w 314
12.5%
o 314
12.5%
r 314
12.5%
k 314
12.5%
G 314
12.5%
Common
ValueCountFrequency (%)
314
50.0%
3 212
33.8%
2 102
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 314
10.0%
e 314
10.0%
t 314
10.0%
w 314
10.0%
o 314
10.0%
r 314
10.0%
k 314
10.0%
314
10.0%
G 314
10.0%
3 212
6.8%

calls_drop_s
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0732484
Minimum0
Maximum39
Zeros112
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:07:59.759269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11.35
Maximum39
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2985366
Coefficient of variation (CV)1.7240834
Kurtosis17.115266
Mean3.0732484
Median Absolute Deviation (MAD)1
Skewness3.6412387
Sum965
Variance28.07449
MonotonicityNot monotonic
2023-07-11T01:08:00.027178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 112
35.7%
1 64
20.4%
2 32
 
10.2%
3 20
 
6.4%
4 18
 
5.7%
6 17
 
5.4%
5 11
 
3.5%
8 9
 
2.9%
11 5
 
1.6%
7 5
 
1.6%
Other values (14) 21
 
6.7%
ValueCountFrequency (%)
0 112
35.7%
1 64
20.4%
2 32
 
10.2%
3 20
 
6.4%
4 18
 
5.7%
5 11
 
3.5%
6 17
 
5.4%
7 5
 
1.6%
8 9
 
2.9%
9 2
 
0.6%
ValueCountFrequency (%)
39 1
0.3%
36 1
0.3%
34 1
0.3%
27 2
0.6%
22 1
0.3%
21 1
0.3%
17 2
0.6%
16 1
0.3%
15 2
0.6%
14 1
0.3%

calls_failure_s
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2101911
Minimum0
Maximum88
Zeros86
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:08:00.324234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile19.7
Maximum88
Range88
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.0696108
Coefficient of variation (CV)1.5488128
Kurtosis36.28274
Mean5.2101911
Median Absolute Deviation (MAD)3
Skewness4.5138512
Sum1636
Variance65.118618
MonotonicityNot monotonic
2023-07-11T01:08:00.579224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 86
27.4%
1 44
14.0%
3 32
 
10.2%
2 23
 
7.3%
4 18
 
5.7%
5 14
 
4.5%
6 14
 
4.5%
7 13
 
4.1%
8 9
 
2.9%
16 8
 
2.5%
Other values (20) 53
16.9%
ValueCountFrequency (%)
0 86
27.4%
1 44
14.0%
2 23
 
7.3%
3 32
 
10.2%
4 18
 
5.7%
5 14
 
4.5%
6 14
 
4.5%
7 13
 
4.1%
8 9
 
2.9%
9 7
 
2.2%
ValueCountFrequency (%)
88 1
 
0.3%
38 1
 
0.3%
32 1
 
0.3%
30 2
0.6%
28 1
 
0.3%
27 1
 
0.3%
26 1
 
0.3%
25 1
 
0.3%
23 2
0.6%
22 3
1.0%

calls_out_month_p
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.08917
Minimum1
Maximum9665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-07-11T01:08:00.996454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q192
median320
Q31212.5
95-th percentile4459.5
Maximum9665
Range9664
Interquartile range (IQR)1120.5

Descriptive statistics

Standard deviation1512.7383
Coefficient of variation (CV)1.5141174
Kurtosis7.6452451
Mean999.08917
Median Absolute Deviation (MAD)270
Skewness2.5399569
Sum313714
Variance2288377
MonotonicityNot monotonic
2023-07-11T01:08:01.455768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 4
 
1.3%
15 4
 
1.3%
13 3
 
1.0%
64 3
 
1.0%
75 3
 
1.0%
285 3
 
1.0%
875 3
 
1.0%
70 3
 
1.0%
815 3
 
1.0%
205 3
 
1.0%
Other values (238) 282
89.8%
ValueCountFrequency (%)
1 1
 
0.3%
2 2
0.6%
4 1
 
0.3%
5 1
 
0.3%
8 1
 
0.3%
10 2
0.6%
13 3
1.0%
15 4
1.3%
16 2
0.6%
19 1
 
0.3%
ValueCountFrequency (%)
9665 1
0.3%
8965 1
0.3%
6805 1
0.3%
6545 1
0.3%
6165 1
0.3%
6155 1
0.3%
5605 1
0.3%
5545 1
0.3%
5475 1
0.3%
5305 1
0.3%

calls_in_month_p
Real number (ℝ)

Distinct246
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869.81529
Minimum1
Maximum8515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-07-11T01:08:01.855020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1102.5
median325
Q31302.5
95-th percentile3216
Maximum8515
Range8514
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation1193.5858
Coefficient of variation (CV)1.3722291
Kurtosis9.3106877
Mean869.81529
Median Absolute Deviation (MAD)276
Skewness2.5968797
Sum273122
Variance1424647.2
MonotonicityNot monotonic
2023-07-11T01:08:02.397548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
835 6
 
1.9%
2045 4
 
1.3%
525 3
 
1.0%
2055 3
 
1.0%
29 3
 
1.0%
85 3
 
1.0%
105 3
 
1.0%
127 2
 
0.6%
71 2
 
0.6%
1135 2
 
0.6%
Other values (236) 283
90.1%
ValueCountFrequency (%)
1 1
0.3%
2 2
0.6%
5 2
0.6%
12 1
0.3%
13 1
0.3%
15 1
0.3%
16 2
0.6%
19 1
0.3%
23 1
0.3%
25 2
0.6%
ValueCountFrequency (%)
8515 1
0.3%
7185 1
0.3%
5915 1
0.3%
5325 1
0.3%
5065 1
0.3%
4615 1
0.3%
4605 1
0.3%
4375 1
0.3%
4285 1
0.3%
3955 1
0.3%

calls_out_tot_p
Real number (ℝ)

Distinct313
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3708678 × 108
Minimum1
Maximum9.6774194 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-07-11T01:08:02.892318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27817072
Q12.8183244 × 108
median4.6422244 × 108
Q36.1140715 × 108
95-th percentile7.7574354 × 108
Maximum9.6774194 × 108
Range9.6774193 × 108
Interquartile range (IQR)3.2957471 × 108

Descriptive statistics

Standard deviation2.2987944 × 108
Coefficient of variation (CV)0.52593547
Kurtosis-0.81373627
Mean4.3708678 × 108
Median Absolute Deviation (MAD)1.6464831 × 108
Skewness-0.27224768
Sum1.3724525 × 1011
Variance5.2844557 × 1016
MonotonicityNot monotonic
2023-07-11T01:08:03.461523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.6%
701202593 1
 
0.3%
264571268 1
 
0.3%
628873259 1
 
0.3%
149694669 1
 
0.3%
607061041 1
 
0.3%
736522837 1
 
0.3%
470161535 1
 
0.3%
584866526 1
 
0.3%
751788995 1
 
0.3%
Other values (303) 303
96.5%
ValueCountFrequency (%)
1 2
0.6%
15 1
0.3%
95 1
0.3%
625 1
0.3%
296875 1
0.3%
2247191 1
0.3%
3890828 1
0.3%
5267445 1
0.3%
17190153 1
0.3%
18046252 1
0.3%
ValueCountFrequency (%)
967741935 1
0.3%
881033485 1
0.3%
867244262 1
0.3%
831361576 1
0.3%
816267547 1
0.3%
810202636 1
0.3%
808630714 1
0.3%
805810171 1
0.3%
805092997 1
0.3%
804761905 1
0.3%

calls_in_tot_p
Real number (ℝ)

UNIQUE 

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6899921 × 108
Minimum5
Maximum9.8280985 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-07-11T01:08:04.038310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile37895642
Q13.1144284 × 108
median4.7686069 × 108
Q36.3535003 × 108
95-th percentile8.3979174 × 108
Maximum9.8280985 × 108
Range9.8280984 × 108
Interquartile range (IQR)3.2390719 × 108

Descriptive statistics

Standard deviation2.3460071 × 108
Coefficient of variation (CV)0.50021558
Kurtosis-0.56601882
Mean4.6899921 × 108
Median Absolute Deviation (MAD)1.6437544 × 108
Skewness-0.12344565
Sum1.4726575 × 1011
Variance5.5037494 × 1016
MonotonicityNot monotonic
2023-07-11T01:08:04.472284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298797407 1
 
0.3%
248211005 1
 
0.3%
9 1
 
0.3%
371126741 1
 
0.3%
850305331 1
 
0.3%
392938959 1
 
0.3%
263477163 1
 
0.3%
529838465 1
 
0.3%
415133474 1
 
0.3%
735428732 1
 
0.3%
Other values (304) 304
96.8%
ValueCountFrequency (%)
5 1
0.3%
9 1
0.3%
85 1
0.3%
99 1
0.3%
375 1
0.3%
703125 1
0.3%
6109172 1
0.3%
10430976 1
0.3%
23728635 1
0.3%
27566051 1
0.3%
ValueCountFrequency (%)
982809847 1
0.3%
976923077 1
0.3%
971428571 1
0.3%
958646617 1
0.3%
928834322 1
0.3%
926818819 1
0.3%
919028476 1
0.3%
915402764 1
0.3%
902236397 1
0.3%
901834239 1
0.3%

no_answer_calls_p
Real number (ℝ)

ZEROS 

Distinct308
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44698542
Minimum0
Maximum3.1750709 × 108
Zeros5
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-07-11T01:08:04.777114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1040155.1
Q115258194
median31280424
Q356612621
95-th percentile1.3359878 × 108
Maximum3.1750709 × 108
Range3.1750709 × 108
Interquartile range (IQR)41354427

Descriptive statistics

Standard deviation47038069
Coefficient of variation (CV)1.0523401
Kurtosis9.6100053
Mean44698542
Median Absolute Deviation (MAD)18657326
Skewness2.6487695
Sum1.4035342 × 1010
Variance2.2125799 × 1015
MonotonicityNot monotonic
2023-07-11T01:08:05.079091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
1.6%
17857143 2
 
0.6%
35714286 2
 
0.6%
32689059 1
 
0.3%
28580074 1
 
0.3%
14596926 1
 
0.3%
76306544 1
 
0.3%
51643497 1
 
0.3%
26871304 1
 
0.3%
215090417 1
 
0.3%
Other values (298) 298
94.9%
ValueCountFrequency (%)
0 5
1.6%
5 1
 
0.3%
7 1
 
0.3%
1125 1
 
0.3%
1625 1
 
0.3%
140625 1
 
0.3%
367061 1
 
0.3%
403811 1
 
0.3%
618048 1
 
0.3%
990099 1
 
0.3%
ValueCountFrequency (%)
317507093 1
0.3%
298988622 1
0.3%
282113505 1
0.3%
252380952 1
0.3%
224534161 1
0.3%
215090417 1
0.3%
175324675 1
0.3%
173387097 1
0.3%
172857143 1
0.3%
169472986 1
0.3%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
164 
-1
150 

Length

Max length2
Median length1
Mean length1.477707
Min length1

Characters and Unicode

Total characters464
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
1 164
52.2%
-1 150
47.8%

Length

2023-07-11T01:08:05.401133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:05.670799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 314
100.0%

Most occurring characters

ValueCountFrequency (%)
1 314
67.7%
- 150
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
67.7%
Dash Punctuation 150
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 314
67.7%
- 150
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 314
67.7%
- 150
32.3%

duration_all_in_a
Real number (ℝ)

UNIQUE 

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2644717 × 109
Minimum0
Maximum9.9278096 × 109
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:05.920485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0465262 × 108
Q11.2936601 × 109
median4.4322067 × 109
Q36.7178889 × 109
95-th percentile9.0972125 × 109
Maximum9.9278096 × 109
Range9.9278096 × 109
Interquartile range (IQR)5.4242287 × 109

Descriptive statistics

Standard deviation2.9173001 × 109
Coefficient of variation (CV)0.68409413
Kurtosis-1.2944817
Mean4.2644717 × 109
Median Absolute Deviation (MAD)2.8385026 × 109
Skewness0.17698729
Sum1.3390441 × 1012
Variance8.5106396 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:06.246546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8955392636 1
 
0.3%
3232850242 1
 
0.3%
1948333333 1
 
0.3%
1046868099 1
 
0.3%
1139911914 1
 
0.3%
5296889379 1
 
0.3%
3157894737 1
 
0.3%
4883310034 1
 
0.3%
7097800553 1
 
0.3%
3402399861 1
 
0.3%
Other values (304) 304
96.8%
ValueCountFrequency (%)
0 1
0.3%
296 1
0.3%
558 1
0.3%
3025 1
0.3%
5395 1
0.3%
40225 1
0.3%
751875 1
0.3%
758375 1
0.3%
28408125 1
0.3%
109058191 1
0.3%
ValueCountFrequency (%)
9927809633 1
0.3%
9865351933 1
0.3%
9860149297 1
0.3%
9849215686 1
0.3%
9813667911 1
0.3%
9783636364 1
0.3%
9679701441 1
0.3%
9663759201 1
0.3%
9592491079 1
0.3%
9577307692 1
0.3%

duration_all_out_a
Real number (ℝ)

Distinct313
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2969145 × 109
Minimum13
Maximum9.9120161 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:06.590870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile160205
Q11.3603867 × 109
median2.7455922 × 109
Q34.7556636 × 109
95-th percentile8.604627 × 109
Maximum9.9120161 × 109
Range9.9120161 × 109
Interquartile range (IQR)3.3952769 × 109

Descriptive statistics

Standard deviation2.5556957 × 109
Coefficient of variation (CV)0.77517802
Kurtosis-0.10013355
Mean3.2969145 × 109
Median Absolute Deviation (MAD)1.5841312 × 109
Skewness0.81081534
Sum1.0352312 × 1012
Variance6.5315804 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:06.923320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 2
 
0.6%
2748645511 1
 
0.3%
1775040776 1
 
0.3%
199 1
 
0.3%
569842643 1
 
0.3%
6121428571 1
 
0.3%
2170888889 1
 
0.3%
2946908814 1
 
0.3%
3078656126 1
 
0.3%
5603540455 1
 
0.3%
Other values (303) 303
96.5%
ValueCountFrequency (%)
13 1
0.3%
20 1
0.3%
25 1
0.3%
26 2
0.6%
31 1
0.3%
45 1
0.3%
96 1
0.3%
199 1
0.3%
535 1
0.3%
2385 1
0.3%
ValueCountFrequency (%)
9912016112 1
0.3%
9860304143 1
0.3%
9833333333 1
0.3%
9708859358 1
0.3%
9662555972 1
0.3%
9621454029 1
0.3%
9596078431 1
0.3%
9590361446 1
0.3%
9589572193 1
0.3%
9578086588 1
0.3%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
177 
-1
137 

Length

Max length2
Median length1
Mean length1.4363057
Min length1

Characters and Unicode

Total characters451
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row1
3rd row-1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 177
56.4%
-1 137
43.6%

Length

2023-07-11T01:08:07.224023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:07.509798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 314
100.0%

Most occurring characters

ValueCountFrequency (%)
1 314
69.6%
- 137
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
69.6%
Dash Punctuation 137
30.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 314
69.6%
- 137
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 314
69.6%
- 137
30.4%

setup_time_avg_a
Real number (ℝ)

Distinct305
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1360088 × 109
Minimum1
Maximum6.6411955 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:07.747725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q11.5737534 × 109
median2.0982473 × 109
Q32.8092514 × 109
95-th percentile4.3420423 × 109
Maximum6.6411955 × 109
Range6.6411955 × 109
Interquartile range (IQR)1.2354979 × 109

Descriptive statistics

Standard deviation1.3097909 × 109
Coefficient of variation (CV)0.61319546
Kurtosis0.56161425
Mean2.1360088 × 109
Median Absolute Deviation (MAD)6.5332265 × 108
Skewness0.35300182
Sum6.7070677 × 1011
Variance1.7155522 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:08.048013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 4
 
1.3%
15 3
 
1.0%
1 3
 
1.0%
1333333333 2
 
0.6%
2 2
 
0.6%
2082459951 1
 
0.3%
1729946524 1
 
0.3%
1715638892 1
 
0.3%
2426229508 1
 
0.3%
1843373231 1
 
0.3%
Other values (295) 295
93.9%
ValueCountFrequency (%)
1 3
1.0%
2 2
0.6%
11 1
 
0.3%
15 3
1.0%
21 1
 
0.3%
25 1
 
0.3%
28 1
 
0.3%
45 1
 
0.3%
115 1
 
0.3%
125 4
1.3%
ValueCountFrequency (%)
6641195461 1
0.3%
6529940516 1
0.3%
6215468037 1
0.3%
5754541141 1
0.3%
5562651422 1
0.3%
5452380952 1
0.3%
5353189231 1
0.3%
5220125786 1
0.3%
5115634155 1
0.3%
4787575758 1
0.3%

setup_failure_perc_a
Real number (ℝ)

ZEROS 

Distinct249
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7633448 × 109
Minimum0
Maximum9.719186 × 109
Zeros56
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:08.372169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.2650144 × 108
median1.3046844 × 109
Q32.6549362 × 109
95-th percentile5.5555556 × 109
Maximum9.719186 × 109
Range9.719186 × 109
Interquartile range (IQR)2.4284348 × 109

Descriptive statistics

Standard deviation1.85263 × 109
Coefficient of variation (CV)1.050634
Kurtosis1.9080576
Mean1.7633448 × 109
Median Absolute Deviation (MAD)1.1246162 × 109
Skewness1.3696756
Sum5.5369025 × 1011
Variance3.4322377 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:08.681040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
17.8%
2 3
 
1.0%
1666666667 3
 
1.0%
5555555556 2
 
0.6%
877192982 2
 
0.6%
2083333333 2
 
0.6%
1724137931 2
 
0.6%
1515151515 2
 
0.6%
625 2
 
0.6%
1141640867 1
 
0.3%
Other values (239) 239
76.1%
ValueCountFrequency (%)
0 56
17.8%
2 3
 
1.0%
20 1
 
0.3%
25 1
 
0.3%
36 1
 
0.3%
95 1
 
0.3%
125 1
 
0.3%
625 2
 
0.6%
2125 1
 
0.3%
77784909 1
 
0.3%
ValueCountFrequency (%)
9719185977 1
0.3%
9205607477 1
0.3%
7954545455 1
0.3%
7813292745 1
0.3%
7102834542 1
0.3%
6714527027 1
0.3%
6504702194 1
0.3%
6442538905 1
0.3%
6060606061 1
0.3%
6024096386 1
0.3%

dropped_calls_perc_a
Real number (ℝ)

ZEROS 

Distinct242
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4954926 × 109
Minimum0
Maximum8.3592245 × 109
Zeros64
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:08.981752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0390737 × 108
median9.4670716 × 108
Q32.2073147 × 109
95-th percentile5.4265873 × 109
Maximum8.3592245 × 109
Range8.3592245 × 109
Interquartile range (IQR)2.1034074 × 109

Descriptive statistics

Standard deviation1.7342715 × 109
Coefficient of variation (CV)1.1596657
Kurtosis2.6663024
Mean1.4954926 × 109
Median Absolute Deviation (MAD)9.4670716 × 108
Skewness1.6562638
Sum4.6958466 × 1011
Variance3.0076976 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:09.287350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
20.4%
675675676 3
 
1.0%
3333333333 3
 
1.0%
2083333333 2
 
0.6%
925925926 2
 
0.6%
290697674 2
 
0.6%
229357798 2
 
0.6%
25 2
 
0.6%
1515151515 1
 
0.3%
1704240472 1
 
0.3%
Other values (232) 232
73.9%
ValueCountFrequency (%)
0 64
20.4%
1 1
 
0.3%
3 1
 
0.3%
5 1
 
0.3%
16 1
 
0.3%
25 2
 
0.6%
125 1
 
0.3%
625 1
 
0.3%
23625 1
 
0.3%
34965035 1
 
0.3%
ValueCountFrequency (%)
8359224508 1
0.3%
7692307692 1
0.3%
7660818713 1
0.3%
7622592968 1
0.3%
7352941176 1
0.3%
7236227824 1
0.3%
6617647059 1
0.3%
6515421703 1
0.3%
6468209865 1
0.3%
6419196062 1
0.3%

mo_mos_avg_a
Real number (ℝ)

Distinct312
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1281708 × 109
Minimum164
Maximum3.9683333 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-07-11T01:08:09.590190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum164
5-th percentile322.25
Q135825
median3.0279944 × 109
Q33.5242839 × 109
95-th percentile3.67006 × 109
Maximum3.9683333 × 109
Range3.9683332 × 109
Interquartile range (IQR)3.5242481 × 109

Descriptive statistics

Standard deviation1.5951159 × 109
Coefficient of variation (CV)0.74952438
Kurtosis-1.6519898
Mean2.1281708 × 109
Median Absolute Deviation (MAD)6.0038195 × 108
Skewness-0.48812005
Sum6.6824564 × 1011
Variance2.5443948 × 1018
MonotonicityNot monotonic
2023-07-11T01:08:10.964573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336 2
 
0.6%
342 2
 
0.6%
353840812 1
 
0.3%
3620714286 1
 
0.3%
305 1
 
0.3%
3513138272 1
 
0.3%
329 1
 
0.3%
3557222222 1
 
0.3%
3573026316 1
 
0.3%
3607706767 1
 
0.3%
Other values (302) 302
96.2%
ValueCountFrequency (%)
164 1
0.3%
168 1
0.3%
179 1
0.3%
219 1
0.3%
250 1
0.3%
277 1
0.3%
281 1
0.3%
283 1
0.3%
288 1
0.3%
291 1
0.3%
ValueCountFrequency (%)
3968333333 1
0.3%
3779275362 1
0.3%
3748333333 1
0.3%
3744166667 1
0.3%
3728982843 1
0.3%
3716358621 1
0.3%
3714216216 1
0.3%
3706410256 1
0.3%
3690333333 1
0.3%
3689495652 1
0.3%

recharges_month_a
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17836.771
Minimum0
Maximum200000
Zeros22
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-07-11T01:08:11.289265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15000
median12250
Q323000
95-th percentile56175
Maximum200000
Range200000
Interquartile range (IQR)18000

Descriptive statistics

Standard deviation20651.979
Coefficient of variation (CV)1.1578317
Kurtosis21.679792
Mean17836.771
Median Absolute Deviation (MAD)8250
Skewness3.5189566
Sum5600746
Variance4.2650423 × 108
MonotonicityNot monotonic
2023-07-11T01:08:11.618969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
7.0%
10000 14
 
4.5%
7000 13
 
4.1%
15000 12
 
3.8%
2000 10
 
3.2%
7500 8
 
2.5%
3000 8
 
2.5%
1000 7
 
2.2%
2500 6
 
1.9%
5000 6
 
1.9%
Other values (95) 208
66.2%
ValueCountFrequency (%)
0 22
7.0%
500 6
 
1.9%
1000 7
 
2.2%
1500 2
 
0.6%
2000 10
3.2%
2500 6
 
1.9%
3000 8
 
2.5%
3500 5
 
1.6%
4000 6
 
1.9%
4500 3
 
1.0%
ValueCountFrequency (%)
200000 1
0.3%
120000 1
0.3%
102500 1
0.3%
85500 1
0.3%
77500 1
0.3%
76000 1
0.3%
72500 1
0.3%
71500 1
0.3%
69000 1
0.3%
68000 1
0.3%

nr_recharges_month_a
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.407643
Minimum0
Maximum365
Zeros22
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-07-11T01:08:11.933692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9.5
Q335
95-th percentile128.5
Maximum365
Range365
Interquartile range (IQR)32

Descriptive statistics

Standard deviation49.804404
Coefficient of variation (CV)1.637891
Kurtosis13.373418
Mean30.407643
Median Absolute Deviation (MAD)8.5
Skewness3.1805806
Sum9548
Variance2480.4787
MonotonicityNot monotonic
2023-07-11T01:08:12.222570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5 31
 
9.9%
1 26
 
8.3%
15 24
 
7.6%
0 22
 
7.0%
35 21
 
6.7%
2 21
 
6.7%
25 18
 
5.7%
3 15
 
4.8%
6 12
 
3.8%
45 11
 
3.5%
Other values (35) 113
36.0%
ValueCountFrequency (%)
0 22
7.0%
1 26
8.3%
2 21
6.7%
3 15
4.8%
4 11
 
3.5%
5 31
9.9%
6 12
 
3.8%
7 5
 
1.6%
8 7
 
2.2%
9 7
 
2.2%
ValueCountFrequency (%)
365 1
 
0.3%
345 1
 
0.3%
235 1
 
0.3%
215 1
 
0.3%
205 3
1.0%
175 2
0.6%
165 1
 
0.3%
155 2
0.6%
145 2
0.6%
135 2
0.6%

tipo_m1
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
AMIGO
195 
FACIL
119 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1570
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFACIL
2nd rowFACIL
3rd rowAMIGO
4th rowFACIL
5th rowAMIGO

Common Values

ValueCountFrequency (%)
AMIGO 195
62.1%
FACIL 119
37.9%

Length

2023-07-11T01:08:12.534071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:12.780668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
amigo 195
62.1%
facil 119
37.9%

Most occurring characters

ValueCountFrequency (%)
A 314
20.0%
I 314
20.0%
M 195
12.4%
G 195
12.4%
O 195
12.4%
F 119
 
7.6%
C 119
 
7.6%
L 119
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1570
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 314
20.0%
I 314
20.0%
M 195
12.4%
G 195
12.4%
O 195
12.4%
F 119
 
7.6%
C 119
 
7.6%
L 119
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 314
20.0%
I 314
20.0%
M 195
12.4%
G 195
12.4%
O 195
12.4%
F 119
 
7.6%
C 119
 
7.6%
L 119
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 314
20.0%
I 314
20.0%
M 195
12.4%
G 195
12.4%
O 195
12.4%
F 119
 
7.6%
C 119
 
7.6%
L 119
 
7.6%

paq_datos_m1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
248 
1
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

Length

2023-07-11T01:08:12.992337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:13.245223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 248
79.0%
1 66
 
21.0%

paq_990_m1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
214 
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

Length

2023-07-11T01:08:13.493220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:13.761935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

Most occurring characters

ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 214
68.2%
1 100
31.8%

paq_min_m1
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
277 
1
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

Length

2023-07-11T01:08:13.979734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:14.233507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 277
88.2%
1 37
 
11.8%

paq_bund_m1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
297 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Length

2023-07-11T01:08:14.540379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:15.016039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

consumo_granel_m1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
179 
1
135 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

Length

2023-07-11T01:08:15.369985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:15.836370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

Most occurring characters

ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179
57.0%
1 135
43.0%

consumos_voz_m1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
281 
0
33 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

Length

2023-07-11T01:08:16.259007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:16.725760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

Most occurring characters

ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 281
89.5%
0 33
 
10.5%

vlr_cargas_m1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17360.51
Minimum0
Maximum136000
Zeros36
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-07-11T01:08:17.209673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14000
median10000
Q324750
95-th percentile59350
Maximum136000
Range136000
Interquartile range (IQR)20750

Descriptive statistics

Standard deviation19986.731
Coefficient of variation (CV)1.1512756
Kurtosis8.7658519
Mean17360.51
Median Absolute Deviation (MAD)9000
Skewness2.4601212
Sum5451200
Variance3.9946943 × 108
MonotonicityNot monotonic
2023-07-11T01:08:17.782776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
11.5%
2000 22
 
7.0%
5000 19
 
6.1%
10000 18
 
5.7%
20000 14
 
4.5%
4000 13
 
4.1%
9000 12
 
3.8%
7000 11
 
3.5%
8000 10
 
3.2%
1000 10
 
3.2%
Other values (52) 149
47.5%
ValueCountFrequency (%)
0 36
11.5%
1000 10
 
3.2%
2000 22
7.0%
3000 6
 
1.9%
4000 13
 
4.1%
5000 19
6.1%
6000 2
 
0.6%
7000 11
 
3.5%
8000 10
 
3.2%
9000 12
 
3.8%
ValueCountFrequency (%)
136000 1
0.3%
131000 1
0.3%
110000 1
0.3%
85000 2
0.6%
81000 1
0.3%
79000 1
0.3%
70000 1
0.3%
66000 1
0.3%
64000 2
0.6%
62000 2
0.6%

cant_cargas_m1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7515924
Minimum0
Maximum43
Zeros36
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-07-11T01:08:18.078626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median4
Q38
95-th percentile17.7
Maximum43
Range43
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation6.5709751
Coefficient of variation (CV)1.1424619
Kurtosis7.9524417
Mean5.7515924
Median Absolute Deviation (MAD)3
Skewness2.3776914
Sum1806
Variance43.177713
MonotonicityNot monotonic
2023-07-11T01:08:18.335305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 46
14.6%
1 43
13.7%
0 36
11.5%
3 31
9.9%
4 29
9.2%
5 23
 
7.3%
9 14
 
4.5%
6 11
 
3.5%
7 10
 
3.2%
10 9
 
2.9%
Other values (20) 62
19.7%
ValueCountFrequency (%)
0 36
11.5%
1 43
13.7%
2 46
14.6%
3 31
9.9%
4 29
9.2%
5 23
7.3%
6 11
 
3.5%
7 10
 
3.2%
8 8
 
2.5%
9 14
 
4.5%
ValueCountFrequency (%)
43 1
0.3%
41 1
0.3%
39 1
0.3%
29 1
0.3%
28 1
0.3%
27 1
0.3%
25 1
0.3%
24 1
0.3%
22 1
0.3%
21 1
0.3%

pyp_m1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
297 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Length

2023-07-11T01:08:18.588330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:18.835284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 297
94.6%
1 17
 
5.4%

promo_recarga_m1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
282 
1
32 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

Length

2023-07-11T01:08:19.047724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:19.296656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 282
89.8%
1 32
 
10.2%

dia_sorpresa_m1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
309 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

Length

2023-07-11T01:08:19.522011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:19.780941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 309
98.4%
1 5
 
1.6%

max_rech
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
CONSUMOS_VOZ
150 
PAQ_990
70 
PAQ_DATOS
58 
CONSUMO_GRANEL
25 
PAQ_MIN
 
11

Length

Max length14
Median length12
Mean length10.315287
Min length7

Characters and Unicode

Total characters3239
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAQ_MIN
2nd rowPAQ_DATOS
3rd rowCONSUMOS_VOZ
4th rowPAQ_990
5th rowPAQ_990

Common Values

ValueCountFrequency (%)
CONSUMOS_VOZ 150
47.8%
PAQ_990 70
22.3%
PAQ_DATOS 58
 
18.5%
CONSUMO_GRANEL 25
 
8.0%
PAQ_MIN 11
 
3.5%

Length

2023-07-11T01:08:20.023711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:20.326931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
consumos_voz 150
47.8%
paq_990 70
22.3%
paq_datos 58
 
18.5%
consumo_granel 25
 
8.0%
paq_min 11
 
3.5%

Most occurring characters

ValueCountFrequency (%)
O 558
17.2%
S 383
11.8%
_ 314
9.7%
A 222
 
6.9%
N 211
 
6.5%
M 186
 
5.7%
C 175
 
5.4%
U 175
 
5.4%
V 150
 
4.6%
Z 150
 
4.6%
Other values (11) 715
22.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2715
83.8%
Connector Punctuation 314
 
9.7%
Decimal Number 210
 
6.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 558
20.6%
S 383
14.1%
A 222
 
8.2%
N 211
 
7.8%
M 186
 
6.9%
C 175
 
6.4%
U 175
 
6.4%
V 150
 
5.5%
Z 150
 
5.5%
P 139
 
5.1%
Other values (8) 366
13.5%
Decimal Number
ValueCountFrequency (%)
9 140
66.7%
0 70
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2715
83.8%
Common 524
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 558
20.6%
S 383
14.1%
A 222
 
8.2%
N 211
 
7.8%
M 186
 
6.9%
C 175
 
6.4%
U 175
 
6.4%
V 150
 
5.5%
Z 150
 
5.5%
P 139
 
5.1%
Other values (8) 366
13.5%
Common
ValueCountFrequency (%)
_ 314
59.9%
9 140
26.7%
0 70
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 558
17.2%
S 383
11.8%
_ 314
9.7%
A 222
 
6.9%
N 211
 
6.5%
M 186
 
5.7%
C 175
 
5.4%
U 175
 
5.4%
V 150
 
4.6%
Z 150
 
4.6%
Other values (11) 715
22.1%

target
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Promotor
162 
Detractor
152 

Length

Max length9
Median length8
Mean length8.4840764
Min length8

Characters and Unicode

Total characters2664
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPromotor
2nd rowPromotor
3rd rowDetractor
4th rowPromotor
5th rowPromotor

Common Values

ValueCountFrequency (%)
Promotor 162
51.6%
Detractor 152
48.4%

Length

2023-07-11T01:08:20.590197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T01:08:20.871230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
promotor 162
51.6%
detractor 152
48.4%

Most occurring characters

ValueCountFrequency (%)
o 638
23.9%
r 628
23.6%
t 466
17.5%
P 162
 
6.1%
m 162
 
6.1%
D 152
 
5.7%
e 152
 
5.7%
a 152
 
5.7%
c 152
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2350
88.2%
Uppercase Letter 314
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 638
27.1%
r 628
26.7%
t 466
19.8%
m 162
 
6.9%
e 152
 
6.5%
a 152
 
6.5%
c 152
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
P 162
51.6%
D 152
48.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2664
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 638
23.9%
r 628
23.6%
t 466
17.5%
P 162
 
6.1%
m 162
 
6.1%
D 152
 
5.7%
e 152
 
5.7%
a 152
 
5.7%
c 152
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 638
23.9%
r 628
23.6%
t 466
17.5%
P 162
 
6.1%
m 162
 
6.1%
D 152
 
5.7%
e 152
 
5.7%
a 152
 
5.7%
c 152
 
5.7%

Interactions

2023-07-11T01:07:42.989978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:40.758601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:49.208104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:54.199327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:00.728840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:05.739952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:11.918267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:17.707615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:22.734675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:29.386705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:34.499529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:40.332110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:46.609262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:51.720893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:59.563203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:04.636619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:12.210075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:17.145181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:25.085079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:30.438945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:36.857104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:43.221485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:40.981562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:49.438547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:54.419799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:00.935801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:05.972472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:12.278924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:17.924775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:22.966944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:29.613901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:34.718683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:40.688184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:46.853343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:52.000746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:59.778757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:04.857672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:12.432802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:17.395187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:25.312765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:30.671178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:37.967540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:43.455090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:41.211957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:49.670457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:54.638695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:01.167305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:06.235646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:12.641278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:18.141072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:23.179162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:29.836306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:34.941583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:41.076003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:47.078115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:52.364436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:00.007091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:05.099456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:12.669438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:17.650131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:25.580269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:30.901210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:38.208314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:43.676623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:41.429462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:49.896775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:54.873751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:01.386868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:06.489732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:13.000947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:18.373581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:23.405025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:30.082349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:35.168339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:41.487685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:47.289839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:52.722634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:00.276470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:05.592320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:12.876150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:17.883136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:25.807344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:31.145813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:38.483226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:43.902146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:41.634374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:50.110772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:55.123879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:01.597317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:06.733262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:13.322533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:18.588592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:23.629950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:30.319621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:35.425865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:41.788323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:47.527386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:53.052880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:00.511327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:06.124366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:13.104347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:18.121743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:26.061678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:31.380417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:38.730850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:44.149996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:41.878015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:50.375443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:55.395165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-11T01:07:15.476684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:23.245407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:28.637570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:34.252005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:41.213763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:46.524613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:44.669006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:52.732726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:58.592885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:04.215518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:09.532718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:16.221575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:21.278511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:27.909974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:33.001754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:38.245474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:45.077189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:50.195071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:57.224550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:03.161456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:10.738087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:15.717586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:23.497351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:28.878159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:34.587509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:41.472419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:46.745006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:44.962403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:52.974351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:58.943511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:04.476527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:10.135958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:16.471297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:21.501354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:28.151029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:33.239038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:38.538105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:45.324638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:50.441552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:57.506095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:03.384889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:10.966867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:15.927513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:23.728652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:29.141467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:34.963352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:41.710962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:47.059969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:45.392643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:53.237895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:59.374776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:04.731977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:10.415336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:16.739477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:21.763853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:28.412579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:33.502180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:38.886750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:45.599694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:50.716196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:57.865599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:03.638598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:11.218652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:16.186628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:24.005840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:29.403675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:35.401952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:41.970088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:47.426753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:45.789750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:53.491907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:59.791056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:04.983713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:10.727172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:16.991641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:22.018716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:28.665919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:33.754450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:39.301117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:45.856196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:50.969226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:58.345758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:03.894497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:11.479685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:16.438479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:24.279172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:29.673647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:35.733538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:42.241430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:47.773226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:48.727541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:53.729549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:00.188656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:05.245937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:11.133684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:17.228896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:22.261820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:28.909121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:33.998865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:39.708743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:46.113431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:51.225917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:59.028820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:04.155323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:11.723809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:16.686426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:24.557893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:29.934687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:36.150338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:42.518244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:48.112359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:48.973503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:05:53.981199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:00.524104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:05.517720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:11.543854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:17.475741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:22.514971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:29.160017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:34.269021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:40.038184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:46.374285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:51.475541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:06:59.342777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:04.420434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:11.988441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:16.922454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:24.822716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:30.202663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:36.551904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T01:07:42.760886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T01:08:21.120265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
calidad_produccalif_vozsenal_vozestabil_llamadacalls_drop_scalls_failure_scalls_out_month_pcalls_in_month_pcalls_out_tot_pcalls_in_tot_pno_answer_calls_pmonth_voice_out_cduration_all_in_aduration_all_out_aduration_all_inout_csetup_time_avg_asetup_failure_perc_adropped_calls_perc_amo_mos_avg_arecharges_month_anr_recharges_month_apaq_datos_m1paq_990_m1paq_min_m1paq_bund_m1consumo_granel_m1consumos_voz_m1vlr_cargas_m1cant_cargas_m1pyp_m1promo_recarga_m1dia_sorpresa_m1
calidad_produc1.0000.5910.5260.486-0.143-0.026-0.0160.0080.0080.111-0.0030.0750.007-0.086-0.063-0.027-0.009-0.049-0.046-0.014-0.013-0.036-0.0490.011-0.0760.0160.017-0.0160.0400.012-0.079-0.099
calif_voz0.5911.0000.6110.634-0.151-0.035-0.006-0.031-0.1100.1120.0180.090-0.065-0.0460.0310.0440.020-0.060-0.023-0.085-0.0290.017-0.0090.056-0.0560.077-0.003-0.070-0.0130.049-0.027-0.103
senal_voz0.5260.6111.0000.661-0.160-0.048-0.042-0.0980.0430.0350.0430.0150.016-0.079-0.004-0.0150.094-0.060-0.015-0.0340.064-0.0330.027-0.008-0.0130.055-0.004-0.049-0.003-0.0300.014-0.097
estabil_llamada0.4860.6340.6611.000-0.076-0.0120.042-0.012-0.0080.1250.0050.0310.0530.027-0.0620.025-0.020-0.0480.062-0.052-0.0060.038-0.0100.0420.0330.0910.022-0.048-0.0290.004-0.037-0.019
calls_drop_s-0.143-0.151-0.160-0.0761.0000.4740.2790.1910.264-0.223-0.1080.0290.0110.197-0.0280.1490.1500.3360.0850.2810.1790.012-0.0640.2300.1240.0210.1680.3550.3240.0180.1210.224
calls_failure_s-0.026-0.035-0.048-0.0120.4741.0000.3000.2900.205-0.103-0.064-0.0380.0810.1170.0670.1390.2450.0710.0680.2740.1740.0130.0150.2040.1790.1350.1300.3580.3580.0570.0430.230
calls_out_month_p-0.016-0.006-0.0420.0420.2790.3001.0000.3890.331-0.187-0.092-0.0500.0640.1300.0870.1190.0760.0580.1310.3700.1990.1290.0060.191-0.0250.1080.1890.3530.427-0.0040.0290.308
calls_in_month_p0.008-0.031-0.098-0.0120.1910.2900.3891.0000.0120.071-0.1250.036-0.0320.067-0.0250.142-0.035-0.0330.1150.3760.1610.1090.0390.234-0.0000.1890.0530.3780.3570.0120.0030.405
calls_out_tot_p0.008-0.1100.043-0.0080.2640.2050.3310.0121.000-0.2760.101-0.0700.1040.117-0.0330.2850.1700.1090.2060.3380.244-0.0500.0550.1700.0180.0230.2310.3030.3040.096-0.0210.108
calls_in_tot_p0.1110.1120.0350.125-0.223-0.103-0.1870.071-0.2761.0000.006-0.0670.065-0.006-0.071-0.220-0.112-0.109-0.163-0.208-0.156-0.0210.007-0.0580.0240.027-0.151-0.159-0.167-0.058-0.011-0.055
no_answer_calls_p-0.0030.0180.0430.005-0.108-0.064-0.092-0.1250.1010.0061.000-0.0600.008-0.0420.0160.0160.022-0.081-0.146-0.163-0.024-0.0800.021-0.0470.010-0.170-0.169-0.154-0.066-0.066-0.064-0.073
month_voice_out_c0.0750.0900.0150.0310.029-0.038-0.0500.036-0.070-0.067-0.0601.000-0.0610.050-0.096-0.019-0.0610.0150.075-0.0440.0720.0870.0240.1120.144-0.0190.0670.0210.024-0.025-0.0360.020
duration_all_in_a0.007-0.0650.0160.0530.0110.0810.064-0.0320.1040.0650.008-0.0611.0000.042-0.034-0.0230.037-0.0320.1420.0790.067-0.0380.123-0.0380.0180.0630.0650.0630.130-0.087-0.007-0.002
duration_all_out_a-0.086-0.046-0.0790.0270.1970.1170.1300.0670.117-0.006-0.0420.0500.0421.000-0.1380.1020.0570.0940.0750.0910.0780.0540.0510.1220.0710.0690.1010.1200.0970.0550.1450.080
duration_all_inout_c-0.0630.031-0.004-0.062-0.0280.0670.087-0.025-0.033-0.0710.016-0.096-0.034-0.1381.000-0.033-0.0450.011-0.0250.0710.019-0.003-0.046-0.097-0.073-0.066-0.0920.0420.0350.040-0.0640.009
setup_time_avg_a-0.0270.044-0.0150.0250.1490.1390.1190.1420.285-0.2200.016-0.019-0.0230.102-0.0331.0000.1180.0860.0620.0970.0090.0790.0030.0520.0460.0860.2180.1480.1210.0770.1180.064
setup_failure_perc_a-0.0090.0200.094-0.0200.1500.2450.076-0.0350.170-0.1120.022-0.0610.0370.057-0.0450.1181.0000.0970.1080.1390.0320.0750.1250.0940.0530.1200.1500.1080.0980.0310.0470.048
dropped_calls_perc_a-0.049-0.060-0.060-0.0480.3360.0710.058-0.0330.109-0.109-0.0810.015-0.0320.0940.0110.0860.0971.0000.0820.1020.0370.0490.0530.1020.0240.0760.1530.0880.011-0.038-0.0240.024
mo_mos_avg_a-0.046-0.023-0.0150.0620.0850.0680.1310.1150.206-0.163-0.1460.0750.1420.075-0.0250.0620.1080.0821.0000.2530.1200.0650.1430.057-0.0890.1370.1890.2210.1590.039-0.0680.020
recharges_month_a-0.014-0.085-0.034-0.0520.2810.2740.3700.3760.338-0.208-0.163-0.0440.0790.0910.0710.0970.1390.1020.2531.0000.3620.2160.2140.1870.1050.2350.1070.8080.5710.194-0.0260.235
nr_recharges_month_a-0.013-0.0290.064-0.0060.1790.1740.1990.1610.244-0.156-0.0240.0720.0670.0780.0190.0090.0320.0370.1200.3621.0000.1170.0870.1780.0370.1290.1670.3540.5830.0210.0090.157
paq_datos_m1-0.0360.017-0.0330.0380.0120.0130.1290.109-0.050-0.021-0.0800.087-0.0380.054-0.0030.0790.0750.0490.0650.2160.1171.0000.2350.0780.1530.4050.1260.2060.241-0.020-0.019-0.003
paq_990_m1-0.049-0.0090.027-0.010-0.0640.0150.0060.0390.0550.0070.0210.0240.1230.051-0.0460.0030.1250.0530.1430.2140.0870.2351.000-0.0590.1080.5110.1230.2240.2280.018-0.095-0.087
paq_min_m10.0110.056-0.0080.0420.2300.2040.1910.2340.170-0.058-0.0470.112-0.0380.122-0.0970.0520.0940.1020.0570.1870.1780.078-0.0591.0000.1740.0420.1250.1930.2760.0440.1710.348
paq_bund_m1-0.076-0.056-0.0130.0330.1240.179-0.025-0.0000.0180.0240.0100.1440.0180.071-0.0730.0460.0530.024-0.0890.1050.0370.1530.1080.1741.0000.1330.0820.1660.0930.0050.0120.082
consumo_granel_m10.0160.0770.0550.0910.0210.1350.1080.1890.0230.027-0.170-0.0190.0630.069-0.0660.0860.1200.0760.1370.2350.1290.4050.5110.0420.1331.0000.2140.2590.2800.020-0.0800.044
consumos_voz_m10.017-0.003-0.0040.0220.1680.1300.1890.0530.231-0.151-0.1690.0670.0650.101-0.0920.2180.1500.1530.1890.1070.1670.1260.1230.1250.0820.2141.0000.1920.262-0.0100.0810.044
vlr_cargas_m1-0.016-0.070-0.049-0.0480.3550.3580.3530.3780.303-0.159-0.1540.0210.0630.1200.0420.1480.1080.0880.2210.8080.3540.2060.2240.1930.1660.2590.1921.0000.7010.4030.0070.244
cant_cargas_m10.040-0.013-0.003-0.0290.3240.3580.4270.3570.304-0.167-0.0660.0240.1300.0970.0350.1210.0980.0110.1590.5710.5830.2410.2280.2760.0930.2800.2620.7011.0000.065-0.0290.237
pyp_m10.0120.049-0.0300.0040.0180.057-0.0040.0120.096-0.058-0.066-0.025-0.0870.0550.0400.0770.031-0.0380.0390.1940.021-0.0200.0180.0440.0050.020-0.0100.4030.0651.0000.0590.082
promo_recarga_m1-0.079-0.0270.014-0.0370.1210.0430.0290.003-0.021-0.011-0.064-0.036-0.0070.145-0.0640.1180.047-0.024-0.068-0.0260.009-0.019-0.0950.1710.012-0.0800.0810.007-0.0290.0591.0000.209
dia_sorpresa_m1-0.099-0.103-0.097-0.0190.2240.2300.3080.4050.108-0.055-0.0730.020-0.0020.0800.0090.0640.0480.0240.0200.2350.157-0.003-0.0870.3480.0820.0440.0440.2440.2370.0820.2091.000
2023-07-11T01:08:21.682817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
calidad_produccalif_vozsenal_vozestabil_llamadacalls_drop_scalls_failure_scalls_out_month_pcalls_in_month_pcalls_out_tot_pcalls_in_tot_pno_answer_calls_pmonth_voice_out_cduration_all_in_aduration_all_out_aduration_all_inout_csetup_time_avg_asetup_failure_perc_adropped_calls_perc_amo_mos_avg_arecharges_month_anr_recharges_month_apaq_datos_m1paq_990_m1paq_min_m1paq_bund_m1consumo_granel_m1consumos_voz_m1vlr_cargas_m1cant_cargas_m1pyp_m1promo_recarga_m1dia_sorpresa_m1
calidad_produc1.0000.6010.4940.486-0.090-0.0440.0530.0300.0040.0950.0830.0590.019-0.039-0.056-0.012-0.009-0.031-0.041-0.0040.024-0.054-0.0670.022-0.082-0.0130.019-0.0010.036-0.011-0.086-0.122
calif_voz0.6011.0000.6320.641-0.0920.0100.0190.023-0.0790.0900.0610.092-0.0580.002-0.0000.0350.035-0.0380.035-0.040-0.020-0.004-0.0060.060-0.0700.0600.007-0.0170.0140.059-0.070-0.119
senal_voz0.4940.6321.0000.670-0.077-0.0590.053-0.0710.0370.0100.107-0.0240.014-0.0360.0140.0020.105-0.0310.0340.0010.011-0.0290.034-0.004-0.0160.0510.0070.0090.009-0.0360.016-0.107
estabil_llamada0.4860.6410.6701.000-0.0220.0540.0850.080-0.0340.0980.0340.0200.0440.042-0.0560.0270.035-0.0130.063-0.0100.0310.044-0.0000.0170.0270.0910.0330.0100.049-0.019-0.038-0.049
calls_drop_s-0.090-0.092-0.077-0.0221.0000.5850.3810.1590.372-0.299-0.0660.0460.0470.192-0.0170.1910.3180.3280.1660.4840.3320.0650.0630.1660.0820.1180.2900.5710.5270.1310.0350.136
calls_failure_s-0.0440.010-0.0590.0540.5851.0000.3370.2490.307-0.154-0.0520.0450.0820.2380.0270.2000.3910.2250.1590.4550.2430.0470.1210.2420.1690.1620.2440.5550.5010.1790.0440.177
calls_out_month_p0.0530.0190.0530.0850.3810.3371.0000.2540.425-0.2670.053-0.0250.1660.1480.0490.1960.2810.2290.2050.5080.3340.1290.1260.1900.0050.1250.3110.4300.4740.068-0.0180.146
calls_in_month_p0.0300.023-0.0710.0800.1590.2490.2541.000-0.0730.146-0.0980.0180.0350.059-0.0410.1110.0750.0310.0880.3070.1890.1020.0160.1250.0320.1650.0080.3000.2850.058-0.0440.138
calls_out_tot_p0.004-0.0790.037-0.0340.3720.3070.425-0.0731.000-0.4610.088-0.0700.0880.146-0.0300.2840.2730.2270.2050.4490.306-0.0480.0420.1760.0060.0120.2190.4040.3620.101-0.0130.114
calls_in_tot_p0.0950.0900.0100.098-0.299-0.154-0.2670.146-0.4611.0000.043-0.0500.074-0.044-0.059-0.236-0.167-0.160-0.149-0.337-0.238-0.0140.013-0.0800.0320.032-0.148-0.278-0.261-0.068-0.013-0.069
no_answer_calls_p0.0830.0610.1070.034-0.066-0.0520.053-0.0980.0880.0431.000-0.1030.046-0.055-0.0110.0310.017-0.107-0.073-0.129-0.012-0.0290.068-0.001-0.008-0.083-0.065-0.095-0.006-0.066-0.062-0.098
month_voice_out_c0.0590.092-0.0240.0200.0460.045-0.0250.018-0.070-0.050-0.1031.000-0.0490.061-0.096-0.047-0.033-0.0070.072-0.0360.0540.0870.0240.1120.144-0.0190.0670.0390.034-0.025-0.0360.020
duration_all_in_a0.019-0.0580.0140.0440.0470.0820.1660.0350.0880.0740.046-0.0491.0000.054-0.029-0.0340.0880.0320.1650.1220.070-0.0420.123-0.0280.0300.0570.0640.1300.152-0.068-0.0060.003
duration_all_out_a-0.0390.002-0.0360.0420.1920.2380.1480.0590.146-0.044-0.0550.0610.0541.000-0.1300.1690.1100.1940.0970.2190.1890.0550.0470.1400.0950.0820.1530.2110.2120.0650.1330.077
duration_all_inout_c-0.056-0.0000.014-0.056-0.0170.0270.049-0.041-0.030-0.059-0.011-0.096-0.029-0.1301.000-0.002-0.0500.012-0.014-0.004-0.072-0.003-0.046-0.097-0.073-0.066-0.092-0.061-0.0290.040-0.0640.009
setup_time_avg_a-0.0120.0350.0020.0270.1910.2000.1960.1110.284-0.2360.031-0.047-0.0340.169-0.0021.0000.2200.1790.0460.2270.1010.1030.0680.0430.0760.1420.2310.2210.2040.0810.1040.063
setup_failure_perc_a-0.0090.0350.1050.0350.3180.3910.2810.0750.273-0.1670.017-0.0330.0880.110-0.0500.2201.0000.1920.1510.3170.2020.0660.1720.1520.0710.1730.2140.3210.2770.0770.0510.084
dropped_calls_perc_a-0.031-0.038-0.031-0.0130.3280.2250.2290.0310.227-0.160-0.107-0.0070.0320.1940.0120.1790.1921.0000.1680.3750.1860.0690.1180.1250.0460.1310.2730.3200.2170.041-0.0540.012
mo_mos_avg_a-0.0410.0350.0340.0630.1660.1590.2050.0880.205-0.149-0.0730.0720.1650.097-0.0140.0460.1510.1681.0000.3710.2160.1190.2180.027-0.0190.2070.1900.3000.2320.022-0.085-0.002
recharges_month_a-0.004-0.0400.001-0.0100.4840.4550.5080.3070.449-0.337-0.129-0.0360.1220.219-0.0040.2270.3170.3750.3711.0000.5840.2210.3290.1930.1300.3570.3270.8420.7050.231-0.0640.178
nr_recharges_month_a0.024-0.0200.0110.0310.3320.2430.3340.1890.306-0.238-0.0120.0540.0700.189-0.0720.1010.2020.1860.2160.5841.0000.2110.1400.0930.0420.2540.2940.5080.6160.058-0.0810.083
paq_datos_m1-0.054-0.004-0.0290.0440.0650.0470.1290.102-0.048-0.014-0.0290.087-0.0420.055-0.0030.1030.0660.0690.1190.2210.2111.0000.2350.0780.1530.4050.1260.2630.338-0.020-0.019-0.003
paq_990_m1-0.067-0.0060.034-0.0000.0630.1210.1260.0160.0420.0130.0680.0240.1230.047-0.0460.0680.1720.1180.2180.3290.1400.2351.000-0.0590.1080.5110.1230.3100.2750.018-0.095-0.087
paq_min_m10.0220.060-0.0040.0170.1660.2420.1900.1250.176-0.080-0.0010.112-0.0280.140-0.0970.0430.1520.1250.0270.1930.0930.078-0.0591.0000.1740.0420.1250.2020.2450.0440.1710.348
paq_bund_m1-0.082-0.070-0.0160.0270.0820.1690.0050.0320.0060.032-0.0080.1440.0300.095-0.0730.0760.0710.046-0.0190.1300.0420.1530.1080.1741.0000.1330.0820.2070.1460.0050.0120.082
consumo_granel_m1-0.0130.0600.0510.0910.1180.1620.1250.1650.0120.032-0.083-0.0190.0570.082-0.0660.1420.1730.1310.2070.3570.2540.4050.5110.0420.1331.0000.2140.3500.3680.020-0.0800.044
consumos_voz_m10.0190.0070.0070.0330.2900.2440.3110.0080.219-0.148-0.0650.0670.0640.153-0.0920.2310.2140.2730.1900.3270.2940.1260.1230.1250.0820.2141.0000.3690.422-0.0100.0810.044
vlr_cargas_m1-0.001-0.0170.0090.0100.5710.5550.4300.3000.404-0.278-0.0950.0390.1300.211-0.0610.2210.3210.3200.3000.8420.5080.2630.3100.2020.2070.3500.3691.0000.8140.300-0.0160.159
cant_cargas_m10.0360.0140.0090.0490.5270.5010.4740.2850.362-0.261-0.0060.0340.1520.212-0.0290.2040.2770.2170.2320.7050.6160.3380.2750.2450.1460.3680.4220.8141.0000.098-0.0880.138
pyp_m1-0.0110.059-0.036-0.0190.1310.1790.0680.0580.101-0.068-0.066-0.025-0.0680.0650.0400.0810.0770.0410.0220.2310.058-0.0200.0180.0440.0050.020-0.0100.3000.0981.0000.0590.082
promo_recarga_m1-0.086-0.0700.016-0.0380.0350.044-0.018-0.044-0.013-0.013-0.062-0.036-0.0060.133-0.0640.1040.051-0.054-0.085-0.064-0.081-0.019-0.0950.1710.012-0.0800.081-0.016-0.0880.0591.0000.209
dia_sorpresa_m1-0.122-0.119-0.107-0.0490.1360.1770.1460.1380.114-0.069-0.0980.0200.0030.0770.0090.0630.0840.012-0.0020.1780.083-0.003-0.0870.3480.0820.0440.0440.1590.1380.0820.2091.000
2023-07-11T01:08:22.239487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
regiontechnologymax_network_voicemonth_voice_out_cpaq_bund_m1dia_sorpresa_m1grupo_edadpaq_datos_m1paq_min_m1genertargetpyp_m1estado_civilduration_all_inout_cpromo_recarga_m1tipo_m1uso_serv_clientemax_rechantiguedconsumo_granel_m1data_usurband_7paq_990_m1consumos_voz_m1
region1.0000.0000.0000.0000.0000.0000.0540.1390.0000.0860.2980.0000.0000.0780.0510.1270.0000.0880.0000.0000.0000.0370.0000.000
technology0.0001.0000.7700.0000.1010.0000.2350.2470.0000.0870.0690.0630.0000.0000.0000.1270.1700.3290.0650.4330.3710.9790.3820.006
max_network_voice0.0000.7701.0000.0000.0710.0000.3480.2950.0000.0840.0000.0690.0000.0000.0000.1000.2170.5030.0530.5110.3390.2870.3900.000
month_voice_out_c0.0000.0000.0001.0000.1170.0000.0000.0550.0850.0000.0260.0000.0000.0690.0000.1380.0000.0000.0350.0000.0000.0000.0000.006
paq_bund_m10.0000.1010.0710.1171.0000.0000.0000.1230.1420.0250.0000.0000.0000.0170.0000.0000.0910.1140.0000.1050.0870.0430.0740.017
dia_sorpresa_m10.0000.0000.0000.0000.0001.0000.0000.0000.3040.0290.0000.0000.0480.0000.1580.0000.0000.3840.0000.0000.0000.0000.0190.000
grupo_edad0.0540.2350.3480.0000.0000.0001.0000.2650.1430.0690.0000.0650.1810.0660.0820.1760.1750.1540.0000.2190.3750.1780.1470.000
paq_datos_m10.1390.2470.2950.0550.1230.0000.2651.0000.0340.0000.0000.0000.0960.0000.0000.1590.1010.5680.0000.3930.3590.0000.2190.098
paq_min_m10.0000.0000.0000.0850.1420.3040.1430.0341.0000.0490.0370.0000.0380.0660.1440.0000.0510.3100.0000.0000.0000.0000.0000.094
gener0.0860.0870.0840.0000.0250.0290.0690.0000.0491.0000.0000.1240.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.000
target0.2980.0690.0000.0260.0000.0000.0000.0000.0370.0001.0000.0890.1090.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.000
pyp_m10.0000.0630.0690.0000.0000.0000.0650.0000.0000.1240.0891.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.0000.000
estado_civil0.0000.0000.0000.0000.0000.0480.1810.0960.0380.0000.1090.0001.0000.0670.0000.0000.0000.0750.0340.0000.0000.0590.0000.088
duration_all_inout_c0.0780.0000.0000.0690.0170.0000.0660.0000.0660.0000.0000.0000.0671.0000.0000.0000.0000.0000.0810.0190.0560.0070.0000.059
promo_recarga_m10.0510.0000.0000.0000.0000.1580.0820.0000.1440.0000.0000.0000.0000.0001.0000.0000.0000.1300.0120.0400.0000.0000.0610.030
tipo_m10.1270.1270.1000.1380.0000.0000.1760.1590.0000.0000.0000.1630.0000.0000.0001.0000.0770.1630.1110.0110.0640.0000.0000.000
uso_serv_cliente0.0000.1700.2170.0000.0910.0000.1750.1010.0510.0000.0000.0000.0000.0000.0000.0771.0000.0530.0930.1150.1190.1170.0000.081
max_rech0.0880.3290.5030.0000.1140.3840.1540.5680.3100.0540.0000.0000.0750.0000.1300.1630.0531.0000.0000.5500.4180.3080.6150.168
antigued0.0000.0650.0530.0350.0000.0000.0000.0000.0000.0000.0380.0000.0340.0810.0120.1110.0930.0001.0000.0000.0000.0000.0000.084
consumo_granel_m10.0000.4330.5110.0000.1050.0000.2190.3930.0000.0000.0000.0000.0000.0190.0400.0110.1150.5500.0001.0000.4550.2160.5020.196
data_usur0.0000.3710.3390.0000.0870.0000.3750.3590.0000.0000.0000.0000.0000.0560.0000.0640.1190.4180.0000.4551.0000.2120.3020.000
band_70.0370.9790.2870.0000.0430.0000.1780.0000.0000.0000.0000.0000.0590.0070.0000.0000.1170.3080.0000.2160.2121.0000.1800.044
paq_990_m10.0000.3820.3900.0000.0740.0190.1470.2190.0000.0000.0000.0000.0000.0000.0610.0000.0000.6150.0000.5020.3020.1801.0000.096
consumos_voz_m10.0000.0060.0000.0060.0170.0000.0000.0980.0940.0000.0000.0000.0880.0590.0300.0000.0810.1680.0840.1960.0000.0440.0961.000

Missing values

2023-07-11T01:07:48.777421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T01:07:50.646100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.